| import os |
| import numpy as np |
| import tensorflow as tf |
| from module import encoder, decoder |
| from glob import glob |
| import runway |
|
|
|
|
| @runway.setup(options={'styleCheckpoint': runway.file(is_directory=True)}) |
| def setup(opts): |
| sess = tf.Session() |
| init_op = tf.global_variables_initializer() |
| sess.run(init_op) |
| with tf.name_scope('placeholder'): |
| input_photo = tf.placeholder(dtype=tf.float32, |
| shape=[1, None, None, 3], |
| name='photo') |
| input_photo_features = encoder(image=input_photo, |
| options={'gf_dim': 32}, |
| reuse=False) |
| output_photo = decoder(features=input_photo_features, |
| options={'gf_dim': 32}, |
| reuse=False) |
| saver = tf.train.Saver() |
| path = opts['styleCheckpoint'] |
| model_name = [p for p in os.listdir(path) if os.path.isdir(os.path.join(path, p))][0] |
| checkpoint_dir = os.path.join(path, model_name, 'checkpoint_long') |
| ckpt = tf.train.get_checkpoint_state(checkpoint_dir) |
| ckpt_name = os.path.basename(ckpt.model_checkpoint_path) |
| saver.restore(sess, os.path.join(checkpoint_dir, ckpt_name)) |
| return dict(sess=sess, input_photo=input_photo, output_photo=output_photo) |
|
|
|
|
| @runway.command('stylize', inputs={'contentImage': runway.image}, outputs={'stylizedImage': runway.image}) |
| def stylize(model, inp): |
| img = inp['contentImage'] |
| img = np.array(img) |
| img = img / 127.5 - 1. |
| img = np.expand_dims(img, axis=0) |
| img = model['sess'].run(model['output_photo'], feed_dict={model['input_photo']: img}) |
| img = (img + 1.) * 127.5 |
| img = img.astype('uint8') |
| img = img[0] |
| return dict(stylizedImage=img) |
|
|
|
|
| if __name__ == '__main__': |
| runway.run() |
|
|
